Patient engagement communicative strategy recommendation

ABSTRACT

Methods and systems for outputting an engagement communicative strategy are described. In an example, a processor may train a communicative model using a patient authored text corpus. The processor may generate at least one patient profile based on the patient authored text corpus and patient authored health data. The processor may construct a knowledge based system based on the communicative model and the at least one patient profile. The processor may input a patient profile of the entity, an engagement degree, and an engagement score to the knowledge based system. The processor may execute the knowledge based system to determine the engagement communicative strategy, and may output the engagement communicative strategy. The engagement communicative strategy may specify a communication scheme to communicate with the entity.

FIELD

The present application relates generally to computers, and computerapplications, and more particularly to computer-implemented methods andsystems relating to health informatics systems.

BACKGROUND

In an example, patient engagement refers to a patient that takes anactive role as a key player in protecting his or her health, choosingappropriate treatments for episodes of ill health, and managing chronicdiseases. In some examples, patients who are engaged tend to use fewerhealthcare resources, make better decisions, and may have better healthoutcomes. In some examples, low patient engagement may result fromcommunication problems between patients and healthcare professionals.

SUMMARY

In some examples, a method for outputting an engagement communicativestrategy is generally described. The method may comprise receiving, by aprocessor, a patient authored text corpus. The method may furthercomprise receiving, by the processor, patient authored health data. Themethod may further comprise training, by the processor, a communicativemodel based on the patient authored text corpus. The method may furthercomprise generating, by the processor, at least one patient profilebased on the patient authored text corpus and the patient authoredhealth data. The method may further comprise constructing, by theprocessor, a knowledge based system based on the communicative model andthe at least one patient profile. The knowledge based system may includean inference engine and a knowledge base. The method may furthercomprise receiving, by the processor, a request for an engagementcommunicative strategy associated with an entity. The method may furthercomprise retrieving, by the processor, a patient profile of the entityfrom the at least one patient profile. The method may further compriseinputting, by the processor, the patient profile, an engagement degree,and an engagement score to the knowledge based system. The engagementdegree may be representative of a level of engagement of the entity in apatient engagement process, and the engagement score may berepresentative of an effectiveness of a strategy to improve the level ofengagement of the entity in the patient engagement process. The methodmay further comprise executing, by the processor, the knowledge basedsystem to determine the engagement communicative strategy associatedwith the entity based on the patient profile, the engagement degree, andthe engagement score. The method may further comprise outputting, by theprocessor, the engagement communicative strategy. The engagementcommunicative strategy may specify a communication scheme to communicatewith the entity.

In some examples, a system effective to output an engagementcommunicative strategy is generally described. The system may comprise amemory configured to store a set of engagement instructions, and aprocessor configured to be in communication with the memory. Theprocessor may be configured to execute the set of engagementinstructions stored in the memory. The processor may be furtherconfigured to receive a patient authored text corpus. The processor maybe further configured to receive patient authored health data. Theprocessor may be further configured to train a communicative model basedon the patient authored text corpus. The processor may be furtherconfigured to generate at least one patient profile based on the patientauthored text corpus and the patient authored health data. The processormay be further configured to construct a knowledge based system based onthe communicative model and the at least one patient profile. Theknowledge based system may include an inference engine and a knowledgebase. The processor may be further configured to receive a request foran engagement communicative strategy associated with an entity. Theprocessor may be further configured to retrieve a patient profile of theentity from the at least one patient profile. The processor may befurther configured to input the patient profile, an engagement degree,and an engagement score to the knowledge based system. The engagementdegree is representative of a level of engagement of the entity in apatient engagement process, and the engagement score may berepresentative of an effectiveness of a strategy to improve the level ofengagement of the entity in the patient engagement process. Theprocessor may be further configured to execute the knowledge basedsystem to determine the engagement communicative strategy associatedwith the entity based on the patient profile, the engagement degree, andthe engagement score. The processor may be further configured to outputthe engagement communicative strategy. The engagement communicativestrategy may specify a communication scheme to communicate with theentity.

In some examples, a computer program product for outputting anengagement communicative strategy is generally described. The computerprogram product may include a computer readable storage medium havingprogram instructions embodied therewith. The program instructions may beexecutable by a processing element of a device to cause the device toperform one or more methods described herein.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example computer system in one embodiment that canbe utilized to implement patient engagement communicative strategyrecommendation in one embodiment.

FIG. 2 illustrates an example process of a phase of an implementation ofthe example system of FIG. 1 in one embodiment.

FIG. 3 illustrates an example process of a phase of an implementation ofthe example system of FIG. 1 in one embodiment.

FIG. 4 illustrates an example process of implementing the example systemof FIG. 1 to train a stage engagement communicative model in oneembodiment.

FIG. 5 illustrates an example process of implementing the example systemof FIG. 1 to define a patient profile in one embodiment.

FIG. 6 illustrates an example process of implementing the example systemof FIG. 1 to classify a patient engagement stage in one embodiment.

FIG. 7 illustrates an example process of implementing the example systemof FIG. 1 to create a knowledge base in one embodiment.

FIG. 8 illustrates an example process of implementing the example systemof FIG. 1 to generate a patient engagement communicative strategy in oneembodiment.

FIG. 9 illustrates a flow diagram relating to patient engagementcommunicative strategy recommendation in one embodiment.

FIG. 10 illustrates a schematic of an example computer or processingsystem that may implement patient engagement communicative strategyrecommendation in one embodiment.

FIG. 11 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 12 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

Improvement of patient engagement may ensure the sustainability ofhealth systems and to improve the population health. In some examples,low patient engagement may occur due to problems of health literacy,lack of treatment decision-making and bad self-management of chronicconditions. Problems in communications between patients (or caregiversof the patients) and healthcare professionals may be one of the causesof the low patient engagement. Thus, methods and systems that help thegovernments, health authorities or healthcare professionals to definethe best patient engagement communicative strategy to apply to one ormore patients are needed to improve health informatics systems, wherethe improved health informatics systems may provide improvements topatient engagement. In an example, communication strategies may bedefined as the blueprints of how information may be exchanged between afirst user and a second user. Thus, a patient engagement communicativestrategy determined from a system in accordance with the presentdisclosure (e.g., system 100 shown in FIG. 1) may improve a healthinformatics system by recommending communication strategies to improvepatient engagement, such as, how to exchange health information betweenone or more patients and healthcare professionals.

System 100 may be implemented to provide assistance on communicationswith one or more patients by defining optimal patient engagementcommunicative strategies. The patient engagement communicative strategyprovided by the system 100 may indicate a strategy to improve how toexchange health information between patients and healthcareprofessionals in order to promote patient engagement. The system 100 mayprovide a cognitive solution to improve the communication between thepatients and the healthcare professionals. The system 100 may combinethe knowledge of the health professionals to create an automated systemto support the healthcare professionals to effectively communicate withthe patient. In an example, healthcare professionals may includepsychologists, physicians, nurses, linguists, social workers, and/orother health professionals. The system 100 may provide improvements tohealth informatics systems by allowing the health informatics systems toperform functions to output communicative strategy in additional tomanagement of patient profiles and healthcare data.

To develop the patient engagement communicative strategy, the system 100may define a patient profile for each patient from an analysis of datareceived from different sources, such as annotated dialog corpus,patient generated health data, patient clinical data, and/or other data.The system 100 may further analyze and classify the defined patientprofile according to engagement stages chosen from a modeling phase. Thesystem 100 may further determine, or calculate, a patient engagementdegree and a score of strategies for patient engagement. The system 100may further determine a personalized patient engagement communicativestrategy based on the patient profile, engagement degree and the scoreof the engagement strategies. In an example, the system 100 may receivean inquiry from a user (e.g., a healthcare professional), and inresponse, may develop and/or select an optimal patient engagementcommunicative strategy based on a context of a patient indicated by theinquiry and a context of the user.

Some example systems may evaluate the patient engagement but fail topropose communicative strategies. Some example systems may proposeautomatic annotation of dialogs in a clinical context but the annotatedialogs may not be used to evaluate the patient engagement or to proposeimprovements on communications with the patients. Some example systemsmay fail to consider patient engagement or a dialog taxonomy tostructure and measure various patient data. Some example systems may usedialog analysis to measure patient adherence to treatment, but may failto offer a communicative strategy to provide suggestions to improve thecommunication between patient and healthcare professionals, and may failto apply to generic cases of health treatment or the use of historicalpatient data to assure a strategic and personalized analysis of thepatient dialogue.

FIG. 1 illustrates an example computer system 100 that can be utilizedto implement patient engagement communicative strategy recommendation,arranged in accordance with at least some embodiments described herein.In some examples, the system 100 may be a health informatics systemimplemented by a computer device 101. The system 100 may include aprocessor 120 and a memory 122 configured to be in communication witheach other. The processor 120 may be a central processing unit of thecomputer device 101. In some examples, the processor 120 may beconfigured to control operations of the memory 122 and/or othercomponents of the computer device 101. In some examples, the computerdevice 101 may include additional hardware components, such asprogrammable logic devices, microcontrollers, memory devices, and/orother hardware components, that may be configured to perform respectivetasks of the methods described in the present disclosure. In someexamples, the processor 120 may be configured to execute softwaremodules that include instructions to perform each respective task of themethods described in the present disclosure. In some examples, theprocessor 120 and/or the memory 122 may be parts of resources providedby a cloud computing platform.

The memory 122 may be configured to selectively store instructionsexecutable by the processor 120. For example, in one embodiment, thememory 122 may store a set of engagement strategy instructions 124,where the engagement strategy instructions 124 may include instructions,such as executable code, related to machine learning algorithms,ontology, graph and network algorithms, text inference, linguisticprocessing algorithms, artificial intelligence, cognitive interactions,and/or other algorithms or techniques, which may implement the system100. The processor 120 may be configured to execute one or more portionsof the engagement strategy instructions 124 in order to facilitateimplementation of the system 100. In some examples, the engagementstrategy instructions 124 may be packaged as a standalone applicationthat may installed on the computer device 101 such that the engagementstrategy instructions 124 may be executed by the processor 120 toimplement the system 100. In some examples, the engagement strategyinstructions 124 may be integrated into an existing health informaticssystem in order for the existing health informatics system to performthe methods described in the present disclosure. In some examples, theengagement strategy instructions 124 may be stored in a programmablehardware component that may be embedded as part of the processor 120and/or the computer device 101.

In some examples, the memory 122 may be further configured to store aplurality of models, such as a health behavior change model 112. In someexamples, the memory 122 may be further configured to store taxonomydata 113 associated with a plurality of communicative taxonomies, ortaxonomies relating to linguistics, communicative acts, etc.

In an example, the system 100 may be implemented in a plurality ofphases—a phase 126 to construct a knowledge based system 130 that mayinclude an inference engine 132 and a knowledge base 134, and a phase128 to execute the knowledge based system 130 to generate a patientengagement communicative strategy 180 corresponding to an entity 110.

In an example, a user 103 (e.g., a healthcare professional) may use auser device 104 to send a request 105 to the computer device 101. Therequest 105 may be a request for the patient engagement communicativestrategy 180 that corresponds to the entity 110, such as a patient. Thecomputer device 101 may receive the request 105 and may generate thepatient engagement communicative strategy 180 corresponding to theentity 110. The computer device 101 may send the patient engagementcommunicative strategy 180 to the user device 104 to fulfill the request105.

The processor 120 may be configured to obtain or receive patientauthored text corpus 106 from a plurality of data sources 102. Thepatient authored text corpus 106 may be a structured set of text datathat may be processed by the processor 120 using the linguisticprocessing algorithms that may be among the engagement instructions 124.The patient authored text corpus 106 may be obtained or received fromdata sources such as social media, healthcare social media,doctor-patient conversations, patient surveys or interviews, patientportals, and/or other sources of information. The patient authored textcorpus 106 may include conversations between patients and one or morehealthcare professionals, interviews of patients, and/or texts that mayinclude relevant information describing opinions, sentiments and pointsof view related to health topics of patients, and/or other textassociated with health-related issues. In some examples, the patientauthored text corpus 106 may include text data associated with theentity 110.

The processor 120 may further obtain or receive patient authored healthdata 107 from the data sources 102, where the patient authored healthdata 107 may include a plurality of data associated with one or morepatients. The data sources 102 that may provide the patient authoredhealth data 107 may include databases storing clinical data and/orhealth data generated by patients. The patient authored health data 107may include clinical data such as patient structured data fromelectronic medical records, electronic health records, or hospitalinformation systems. The patient authored health data 107 may furtherinclude patient generated health data such as health related datacreated, recorded or gathered by or from patients, family members, orother caregivers, to help address health concerns, health history,treatment history, biometric data, symptoms, lifestyle choices, and/orother types of information related to the health of the patients. Insome examples, the patient authored health data 107 may include dataassociated with the entity 110.

In an example, the patient authored text corpus 106 and the patientauthored health data 107 may be obtained or received from differentsources. In some examples, portions of the patient authored text corpus106 may not be associated to particular patients. For example, thepatient authored text corpus 106 may include text from conversationsbetween doctors and patients, without indicating the identities of thedoctors and the patients within the conversations. In some examples, thepatient authored health data 107 may include identities of patients andcorresponding health data. In some examples, the system 100 may alsoreceive other data such as audio data including audio recordings ofdoctor-patient conversations, patient surveys or interviews, and/orother sources of information recorded as audio data.

Obtaining or receiving of information pertaining to patients would beperformed with proper permissions from respective parties involved, forexample, including the patients. For example, prior to retrievingpatient authored text corpus 106 and patient authored health data 107,the processor 120 may first send a data retrieval request to one or moredata sources 102. Upon receiving approval of the data retrieval requestfrom the data sources 102, where the approval may be issued by thepatients who authored the patient authored text corpus 106 and thepatient authored health data 107, the processor 120 may begin toretrieve patient authored text corpus 106 and patient authored healthdata 107 from the data sources 102.

In an example, execution of the phase 126 may include the processor 120annotating the patient authored text corpus 106 to generate annotatedtext data 108. The processor 120 may annotate the patient authored textcorpus 106 in accordance with the health behavior change model 112 and acommunicative taxonomy among taxonomy data 113. In some examples, theprocessor 120 may pre-process the patient authored text corpus 106 priorto performing the annotating, such that the processor 120 may annotatethe pre-processed portions or fragments of the patient authored textcorpus 106.

The processor 120 may use the annotated text data 108 as training datato train a stage engagement communicative model (herein “communicativemodel 131”), where the communicative model 131 may be a model related toa communicative taxonomy of each stage of behavior change of one or morepatients. For example, an input to the communicative model 131 may be adiscussion between a patient and a healthcare professional concerningtreatment or clinical condition, and the discussion may includeparticular communicative action characteristics such as utterancecharacteristics from the patient. The communicative model 131 maydetermine an outcome to classify the behavior of the patient (aspositive or negative perception of patient engagement, or particulardisease, or health related issues associated with the patient) in thediscussion based on the tone, text, communicative actions, and/or othercharacteristics within the discussion. In an example, the processor 120may apply machine learning instructions among the engagementinstructions 124 to train the communicative model 131 (further describedbelow).

The processor 120 may further define a set of patient profiles 140 basedon the communicative model 131, the patient authored text corpus 106,and the patient authored health data 107 (further described below. Eachpatient profile 140 may include data indicating a personality,preference of learning, preference of communication type, and/or otherinformation and preferences, of a corresponding patient. The processor120 may use the communicative model 131 and the set of patient profiles140 to generate the knowledge base 134 of the knowledge based system130. In some examples, the communicative model 131, the patient profiles140, and the knowledge base 134 may be continuously updated by thesystem 100 when the patient authored text corpus 106 and the patientauthored health data 107 are updated. In some examples, the knowledgebase 134 may be represented as an object model, such as ontology and/orgraphs. The knowledge base 134 may represent facts inferred from thepatient authored text corpus 106, the patient authored health data 107,and the set of patient profiles 140. In some examples, the knowledgebase 134 may indicate facts relating to one or more populations, such aspatients from particular groups and/or locations, and may also indicatefacts relating to individual patients, such as the entity 110.

The knowledge based system 130 may include the inference engine 132 andthe knowledge base 134, wherein the inference engine 132 may bedeveloped by the system 100 (further described below). The knowledgebase 134 may be coupled to the inference engine 132, where the inferenceengine 132 may be a set of inference rules (that may be defined byengagement strategy instructions 124) executable by the processor 120infer new information from the knowledge base 134. The processor 120 maycomplete the phase 126 upon the construction of the knowledge basedsystem 130.

In some examples, the phase 128 may be triggered by a receipt of arequest 105. Execution of the phase 128 may include the processor 120determining, or receiving, an engagement degree 160 and an engagementstrategy score 170 associated with the entity 110. The engagement degree160 may indicate a degree, or level, of engagement by the entity 110 ina patient engagement process (e.g., how engaged is the entity 110) In anexample, a current engagement stage of the entity 110 relative to theengagement process, along with opinions or perceptions of the entity 110on health topics or the engagement process, may be used by the processor120 to determine the engagement degree 160. The engagement strategyscore 170 may indicate a score, or effectiveness, of a current strategybeing implemented, or planning to be implemented, on the entity 110 toachieve a next engagement stage in the engagement process. The processor120 may input the patient profile 140, the engagement degree 160, andthe engagement score 170, into the knowledge based system 130. Theprocessor 120 may execute the knowledge based system 130, such as byapplying the inference rules of the inference engine 132, on theinputted data and using knowledge base 134, to generate the patientengagement communicative strategy 180. The patient engagementcommunicative strategy 180 may include a set of data indicating, orspecifying, one or more of the patient profile 140 of the entity 110, acurrent engagement stage of the entity 110, a next engagement stagewithin an engagement process being applied on the entity 110, theengagement degree 160, the engagement score 170, a communication schemethat may include a suggestion of strategies for achieving the nextengagement stage, and/or other data that may provide suggestions toimprove the patient engagement of the entity 110.

The system 100 may deploy the knowledge based system 130 to aninteractive device, such as a computer device implementing a chatbot, inorder for the interactive device to receive inquiries on one or morepatients (e.g., the request 105) and determine answers (e.g., thepatient engagement communicative strategy 180) to the inquiries. Theprocessor 120 may also be configured to render the data among thepatient engagement communicative strategy 180 in order to output, on adisplay, the answers to inquiries received at the interactive system, oroutput a report including the patient engagement communicative strategy180, in order for the user 103 to view the patient engagementcommunicative strategy 180.

In an example, the user 103 may be a health professional who requests toknow (e.g., using the request 105) how much the entity 110 is engaged orsatisfied with a conduct treatment that was agreed upon by the healthprofessional and the entity 110, and how to improve communication, suchas a talking style, to the entity 110 in order to improve or maintain ahigh patient engagement. The health professional may also request, aspart of the request 105, for an evaluation of conversations between thehealth professional and the entity 110. The system 100 may receive therequest 105, and may present to the health professional the profile ofthe entity 110 (e.g., the patient profile 140) in order for the healthprofessional to understand better the entity 110 and personalizetreatment plans of the entity 110. Also, the health professional mayknow how much the entity 110 is engaged to planned treatments based onthe engagement degree 160 determined by the system 100. In additional tothe engagement degree 160, the system 100 may present a currentengagement stage and a next engagement stage and the engagement score170 of one or more strategies to proceed to the next engagement stage,where the next engagement stage may be a goal to improve or maintain thepatient engagement of the entity 110. The health professional may viewthe engagement score 170 of the strategies, and may determine whichstrategy may require improvement. The system 100 may suggestcommunication schemes that may facilitate improvement of the strategies,such as communicate with the entity 110 using a recommendedcommunication tool, method, tone, and/or other strategies.

In an example, the interaction system implementing the system 100 or theknowledge based system 130 may conduct a teleconsultation with thehealth professional to provide consultation on strategies to improve thepatient engagement of the entity 110.

In an example, the system 100 may provide an analysis on the engagementof a particular population, such as a group of patients. The patientengagement communicative strategy 180 generated by the system 100 maypresent information and prioritize interventions with the patients amongthe population according to their engagement degree and score of theengagement strategy. For example, the patient engagement communicativestrategy 180 may indicate a need to communicate with patients among thepopulation that has relatively low engagement degrees beforecommunicating with patients among the population that has relativelyhigh engagement degrees. The health professional, based on the patientengagement communicative strategy 180, may determine whether it ispreferable to communicate with the patients directly, or with a careteam of the patients. The health professional, based on the patientengagement communicative strategy 180, may use a particular style ortool to communicate with the patients among the population based on thecommunications scheme suggested by the patient engagement communicativestrategy 180. For example, the health professional may use thesuggestions in the patient engagement communicative strategy 180 todetermine an optimal communication style with patients of differentpopulations.

In an example, a healthcare provider may use the engagement degree 160provided by the patient engagement communicative strategy 180 toclassify clients and to promote various facets of healthcare, such astreatment options, prices, resources, and/or other facets. For example,the healthcare provider may use the communication scheme suggested bythe patient engagement communicative strategy 180 to promote healthyhabits to help patients with particular health problems (e.g., whattypes of exercise may be appropriate), plan patient-engagement programsto clients, and promote available resources for the patients, and/orother facets of healthcare.

In an example, a chatbot may implement the system 100 to communicatewith the entity 110 directly using the communication scheme suggested bythe patient engagement communicative strategy 180. For example, thechatbot may be programmed to speak at a recommended volume, tone, voice,speed, and/or other characteristics, that may lead to an improvement ofthe patient engagement of the entity 110.

FIG. 2 illustrates an example process of a phase of an implementation ofthe example system of FIG. 1, arranged in accordance with at least someembodiments described herein. FIG. 2 may be described below withreferences to the above descriptions of FIG. 1.

In the example process shown in FIG. 2, the phase 126 of theimplementation of the system 100 may start at block 201, where thesystem 100 may train the communicative model 131. Training of thecommunicative model 131 may be based on annotated portions of thepatient authored text corpus 106 (e.g., annotated text data 108). Thepatient authored text corpus 106 may include text data retrieved fromsocial media, healthcare social media, doctor-patient conversations,patient surveys or interviews, patient portals, texts that containsrelevant information describing patient's opinions, sentiments andpoints of view related to health topics related with diseases and/orother data sources.

In an example, the system 100 may identify particular pieces of datathat may be needed to create the knowledge base 134, and may store theidentified data in a knowledge database 210. The system 100 may receivethe patient authored text corpus 106 for the first time, the system 100may annotate the patient authored text corpus 106 according to thehealth behavior change model 112 in order to generate the annotated textdata 108. Examples of the health behavior change model 112 may includetranstheoretical model, health belief model, theory of planned behavior,and/or other behavior models. When the system receives the same patientauthored text corpus 106 a second time, or at subsequent times,annotation may not be necessary because annotation of the same patientauthored text corpus 106 may already be recorded in the knowledge base134. The processor 120 may store the annotated text data 108 in theknowledge database 210. The knowledge database 210 may be configured tostore data that is necessary to create the knowledge base 134. Bystoring data necessary to create and/or update the knowledge base 134 inthe knowledge database 210, the processor 120 may access data from theknowledge database 210 directly to create and/or update the knowledgebase 134, and may avoid performing excessive operations to retrieve,process, and annotate, data such as the patient authored text corpus 106during creation and updates of the knowledge base 134. The processor 120may execute machine learning algorithms to train the communicative model131 using the annotated text data 108 stored in the knowledge database210.

The phase 126 may continue from block 201 to block 202, where the system100 may define a set of patient profiles 140. The system 100 may definea plurality of patient profiles 140 based on the patient authored textcorpus 106 and the patient authored health data 107 retrieved from thedata sources 102. The patient authored health data 107 may includeclinical data, patient generated health data, and/or other dataassociated with the health of a patient. The patient authored healthdata 107 may also include medical record (EMR), electronic health record(EHR), data from hospital information systems (HIS), health-related datacreated, recorded or gathered by or from patients or family members orother caregivers of the patient, information that may help to address ahealth concern, health history, treatment history, biometric data,symptoms, and lifestyle choices, and/or other information. In someexamples, the patient authored health data 107 may be distinct from datagenerated in clinical settings, and may be generated through encounterswith providers in various ways. For example, a patient may beresponsible for capturing or recording particular data associated withhis or her own health, or the patient may decide how to share ordistribute, or which data to share and distribute, the data generated bythe patient to health care providers and others.

In some examples, the system 100 may generate the patient profile 140based on the annotated text data 108 stored in the knowledge database210 in addition to using the patient authored health data 107. In someexamples, the system 100 may store the patient profile 140 in theknowledge database 210. The plurality of patient profiles 140 generatedand stored in the knowledge database 210 may include patient profiles ofone or more patients. In some examples, the patient profiles 140 may beindexed based on identifications of each patient in the patient authoredhealth data 107.

The phase 126 may continue from block 202 to block 203, where the system100 may create, or generate, the knowledge base 134 based on the datastored in the knowledge database 210. The knowledge base 134 may includea set of structured data, where each piece of data may represent anobject, and each object may include one or more pointers that point toanother object among the knowledge base 134. The objects represented inthe knowledge base 134 may include entities (e.g., a doctor, a patient,and/or other types of entity), items (e.g., tools such as medical tools,treatment tools, therapy tools, computing devices, and/or other items),facts (e.g., a preference, an event, and/or other types of facts),and/or other types of objects. By creating the knowledge base 134 torepresent the objects, and assigning pointers for the objects to expressrelationships among the objects, the knowledge base 134 may provide apool of data representing medical history of patients, preferences ofthe patients, demographics of patients, and/or other facts of thepatients.

The processor 120 may create the knowledge base 134 based on thecommunicative model 131, such as by populating the knowledge base 134with outcomes determined by the communicative model 131 and assigningpointers to connect the outcomes to corresponding inputs (e.g., such asthe annotated text data 108). Further, the processor 120 may create theknowledge base 134 based on the plurality of patient profiles 140 storedin the knowledge database 210, such as by populating the knowledge base134 with entities and facts indicated by each of the patient profiles140. By creating the knowledge base 134 based on both the communicativemodel 131 and the patient profiles 140, the knowledge base 134 may mapportions of the patient profiles 140 to inputs and outputs of thecommunicative model 131, such that the connections may indicate how apatient may behave in particular situations. The processor 120 maycontinuously retrain the communicative model 131 based on new data beingreceived, from the data sources 102, at the system 100, and may updatethe knowledge base 134 based on the retrained communicative model.

The processor 120 may also develop the inference engine 132 (furtherdescribed below). The inference engine 132 may include inference rules,or logic, that may be defined by the engagement instructions 124, andmay be executable by the processor 120 to infer new data from theknowledge base 134. For example, an input to the knowledge based system130 may be an inquiry on a communication preference of the entity 110,and the processor 120 may apply the inference rules in the inferenceengine on the knowledge base 134 to identify data representing theentity 110 and preferred communication method of the entity 110. In someexamples, the processor 120 may create or update the inference engine132 based on changes to the communicative model 131, patient profile140, and the engagement degree 160 (described below) and the engagementstrategy score 170 (described below).

FIG. 3 illustrates an example process of a phase of an implementation ofthe example system of FIG. 1, arranged in accordance with at least someembodiments described herein. FIG. 3 may be described below withreferences to the above descriptions of FIGS. 1-2.

In the example process shown in FIG. 3, the phase 128 of theimplementation of the system 100 may start at block 301. In someexamples, the block 301 may be triggered by the receipt of the request105 at the system 100. At block 301, the processor 120 may define thepatient profile 140 of the entity 110 based on the patient authored textcorpus 106 and the patient authored health data 107. The processor 120may store the patient profile 140 of the entity 110 in an engagementdatabase 320, where the engagement database 320 may be configured tostore data that may be necessary to generate the patient engagementcommunicative strategy 180 to fulfill request 105. In some example, thepatient profile 140 of the entity 110 may already be stored in theengagement database 320 prior to the start of block 301, such that atblock 301, the processor 120 may identify and retrieve the patientprofile 140 of the entity 110 from the engagement database 320.

The phase 128 may continue from block 301 to block 302, where the system100 may classify a patient engagement stage, such as defining a currentpatient engagement stage of the entity 110, based on the patientauthored text corpus 106 and the data stored in the engagement database320. In some examples, a patient engagement process may include aplurality of stages. In an example, stages among a first patientengagement process may include inform, involve, and collaborate. Inanother example, stages among a second patient engagement process mayinclude inform, engage, empower, collaborate, and support. The stages ofthe patient engagement process being used to implement the system 100may be based on a desired implementation of the system 100. In someexamples, the engagement stages may be classified according topreviously chosen model of behavior change. For example, aTranstheoretical Model of Behavior Change (TTM) may include fiveengagement stages: pre-contemplation, contemplation, preparation, actionand maintenance. The classification of the patient engagement stage willbe described in more detail below.

The phase 128 may continue from block 302 to block 303, where the system100 may determine the engagement degree 160 and the score of thestrategies (engagement strategy score 170). In an example, portions ofthe patient authored text corpus 106 may be annotated by the processor120 to generate the annotated text data 108, where the annotated textdata 108 may be classified based on perceptions and polarity, such aspositive and negative perceptions, views, opinions, of health-relatedissues. The processor 120 may classify the annotated text data into oneor more of the perceptions of the health behavior change model 112. Forexample, a piece of annotated text data 108 may indicate a negativeperception of a particular disease from the entity 110, such that theprocessor 120 may classify the piece of annotated text data 108 into anegative perception category. The processor 120 may determine theengagement degree 160 based on the classification of the annotated textdata 108 into the health behavior change model 112, and based on thedata stored in the engagement database 320. The engagement degree 160may be reflective of how engaged is the entity 110 in an engagementprocess being implemented for the entity 110. For example, results ofthe classification of the annotated text data 108 may includeperceptions classified as positive or negative to a patient engagementcontribution, such as a positive perception indicates an increase inpatient engagement and a negative perception indicates a decrease inpatient engagement. Thus, a patient with a significant amount ofnegative perceptions may result in a low engagement degree, which may bereflective of low patient engagement, or reflective of possibleunwillingness from the patient to engage or continue to engage in his orher own health treatments.

In an example, the engagement strategy score 170 may be scores assignedto the entity 110 based on a set of World Health Organization (WHO)rules. The system 100 may store the engagement degree 160 and theengagement strategy score 170 in the engagement database 320. In someexamples, information among the patient authored health data 107, suchas diagnosis, current health state, and/or other information, may beused by the processor 120 to determine the engagement score 170 of eachintervention strategy applicable to the patient (further describedbelow).

The phase 128 may continue from block 303 to block 304, where the system100 may define the patient engagement communicative strategy 180 usingthe knowledge base 134, the patient profile 140, the engagement degree160, and/or the engagement strategy score 170. The patient engagementcommunicative strategy 180 may provide the user 103 recommendations andstrategies to communicate with the entity 110.

FIG. 4 illustrates an example process of implementing the example systemof FIG. 1 to train a stage engagement communicative model, arranged inaccordance with at least some embodiments described herein. FIG. 4 maybe described below with references to the above descriptions of FIGS.1-3.

The processor 120 of the system 100 may train the stage engagementcommunicative model (communicative model 131) by implementing an exampleprocess shown in FIG. 4, which includes blocks 401, 402, 403, 404, 405,406, 407, and 408. In an example, the blocks 401, 402, 403, 404, 405,406, 407, and 408 may be components of the block 201 shown in FIG. 2corresponding to training the model 131 in the phase 126.

At block 401, the processor 120 may receive the patient authored textcorpus 106. The processor 120 may determine whether the patient authoredtext corpus 106 is received at system 100 for the first time. Forexample, the processor 120 may compare one or more portions of thereceived patient authored text corpus 106 with data stored in theknowledge database 210 to determine whether the one or portions arealready stored in the database 210. If the processor 120 determines thatit is the first time the system 100 receives the patient authored textcorpus 106, the processor 120 may proceed to block 402.

At block 402, the processor 120 may pre-process the received patientauthored text corpus in order to prepare the patient authored textcorpus 106 for training the communicative model 131. Various textpre-processing techniques may be applied by the processor 120 to cleanand prepare the patient authored text corpus 106 for laterclassification. Some examples of pre-processing the patient authoredtext corpus 106 may include sentence split, parsing sentences, stemming,and/or other text processing techniques.

At block 403, the processor 120 may select a health behavior changemodel 112 from a plurality of models that may include transtheoreticalmodel, health belief model, theory of planned behavior, and/or otherhealth-related behavior models. The plurality of models may be stored inthe memory 122, and selection criteria that may be used by the processor120 may be part of the engagement instructions 124. In some examples,blocks 402, 403 may be performed by the processor 120 in an arbitraryorder or simultaneously. The selection of the health behavior changemodel may provide a foundation for patient adherence assessment as theplurality of health behavior change models include indications of theperceptions, or points of view, of the patients regarding medicalinfrastructure, disease, treatment and the patients themselves.

Upon completion of blocks 402 and 403, the process to train thecommunicative model 131 may proceed to block 404. At block 404, theprocessor 120 may annotate the patient authored text corpus 106pre-processed at block 402 according to the health behavior change model112 chosen at block 403. The processor 120 may annotate thepre-processed patient authored text corpus 106 to generate the annotatedtext data 108. The processor 120 may annotate the patient authored textcorpus 106 according to perceptions (e.g., by severity, vulnerability,benefits, etc.) of the selected health behavior change model 112. Forexample, if a fragment of the patient authored text corpus 106 indicatesthat the entity 110 has doubts on a treatment plan, according to thehealth behavior change model 112, the processor 120 may annotate thefragment by annotating the treatment plan with a negativeinterpretation. The processor 120 may store the annotated text data 108in the database 210. In some examples, the processor 120 may beconfigured to annotate fragments of the patient authored text corpus 106by adding or attaching metadata to the fragments, where the metadata mayindicate an interpretation of the annotated fragment. The combination ofthe metadata and the annotated fragment may be parts of the annotatedtext data 108.

Returning to block 401, if the processor 120 determines that it is notthe first time the system 100 receives the patient authored text corpus106, the process to train the communicative model 131 may proceed toblock 408. In some examples, the system 100 may perform an update to theknowledge base 134 in response to the determination that it is not thefirst time the system 100 receives the patient authored text corpus. Atblock 408, the processor 120 may retrieve the patient authored textcorpus 106 that may already be annotated and stored in the knowledgedatabase 210. The process to train the model 131 may proceed to block405 from block 408.

At block 405, the processor 120 may further annotate the patientauthored text corpus 106 annotated in block 404 to generate theannotated text data 108. The processor 120 may further annotate thepatient authored text corpus 106 annotated in block 404 according to acommunicative taxonomy, such as speech act, rhetoric approaches, ISOstandards such as ISO 24617, primitives such as dialog act markup inseveral layers (DAMSL, SWBD-DAMSL), and/or other communicative orlinguistic taxonomy. For example, a fragment among the patient authoredtext corpus 106 that includes words or expressions of uncertainty may beannotated as a negative perception. In some examples, the processor 120may execute the blocks 404 and 405 in an arbitrary order orsimultaneously. The engagement instructions 124 may include instructionsfor the processor 120 to determine which communication taxonomy may beused to annotate the patient authored text corpus. In some examples,speech acts or dialog acts may be characterizations of actions performedby a speaker during a conversation or a dialog. The characterizationsprovide representations of conversational function and may be analyzedby computer systems, such as the system 100, in order to develop models(e.g., the communicative model 131) that may be executed toautomatically interpret various communicative acts and to determinemeaningful responses or reactions.

At block 406, the processor 120 may apply machine learning algorithms,using the annotated text data 108 as inputs to the machine learningalgorithms, to train the communicative model 131. For example, theannotated text data 108 may include fragments of the patient authoredtext corpus 106 and corresponding annotations indicating aninterpretation of the fragments (e.g., interpretation such as positiveor negative opinion towards a health topic being discussed in thefragments). The fragments among the annotated text data 108 andcorresponding interpretations may be used as training set that may beinputted into a machine learning algorithm to train the communicativemodel 131. As such, the communicative model 131 may be a classifier toclassify communicative acts, such as fragments of conversations includedin the patient authored text corpus 106, into categories such aspositive or negative perceptions. Machine learning algorithms that maybe applied by the processor 120 to train the communicative model 131 mayinclude naïve Bayes algorithm, support vector machines (SVM), multilayerperceptron (MLP) and/or other machine learning algorithms.

At block 407, a completion of the training of the communicative model131 may provide a model of communicative taxonomy for each stage ofbehavior change corresponding to the one or more patients. In someexamples, the communicative model 131 may be an automatic textclassifier that can be executed, such as by the processor 120, toclassify perceptions indicated by the selected health behavior changemodel 112 into categories of communicative acts.

FIG. 5 illustrates an example process of implementing the example systemof FIG. 1 to define a patient profile, arranged in accordance with atleast some embodiments described herein. FIG. 5 may be described belowwith references to the above descriptions of FIGS. 1-4.

The processor 120 of the system 100 may define the patient profile 140by implementing an example process shown in FIG. 5, which includesblocks 501, 502, and 503.

In some examples, the patient profile 140 may include information of aplurality of attributes of one or more patients, such as facts ordemographics of the entity 110.

At block 501, the processor 120 may obtain text data authored by theentity 110, which may be among the patient authored text corpus 106, andthe patient authored health data 107 associated with the entity 110.Also at block 501, the processor 120 may obtain the annotated text data108, such as by annotating the patient authored text corpus 106 and/orretrieving annotated text data 108 corresponding to the entity 110 thatare stored in the knowledge database 210 and/or the engagement database320.

At block 502, the processor 120 may input the data obtained from block501 into the knowledge based system 130 to infer information about thepatients, where the inferred information may be used by the processor120 to generate one or more patient profiles 140. The processor 120 mayapply inference rules in the inference engine 132 on the data receivedfrom block 501, and on the knowledge base 134, to infer or generate thepatient profiles 140. For example, an input to the knowledge basedsystem 130 may be a conversation between a healthcare professional and apatient suffering from a particular illness. The processor 120 mayexecute the inference engine 132 to analyze data and outputs from theconversation in order to infer that the patient learns best by examplesand likes a clear and precise style of communication. As such, based onthe patient authored text corpus 106 and the knowledge base 134, theinference engine 132 may infer the information to generate patientprofiles 140. The knowledge based system 130 may send the inferredinformation to the processor 120. The processor 120 may continue toexecute the knowledge based system 130 to infer information of one ormore patients to generate the patient profiles 140, such as calling forthe knowledge based system 130 to infer information relating topersonality, preference of learning, and/or other information related tothe patient. In an example, the processor 120 may call the knowledgebased system 130 by executing the inference rules defined by theinference engine 132 of the knowledge based system 130.

At block 503, upon a completion of creating the patient profile 140, theprocessor 120 may store the patient profile 140 in the database 210and/or the as part of the engagement date 320. The patient profile 140may be updated by the processor as new information relating to patientsare received at system 100. The processor 120 may update the patientprofile 140 stored in the database 210 and/or the engagement data 320 aswell. By storing updated patient profile 140 in the database 210 and/orthe engagement data 320, the processor 120 may continuously update theknowledge base 134 using the most up to date version of the patientprofile 140.

FIG. 6 illustrates an example process of implementing the example systemof FIG. 1 to classify a patient engagement stage, arranged in accordancewith at least some embodiments described herein. FIG. 6 may be describedbelow with references to the above descriptions of FIGS. 1-5.

The processor 120 of the system 100 may classify an engagement stage ofthe entity 110, such as determining a current engagement stage of theentity 110 within a patient engagement process, by implementing anexample process shown in FIG. 6, which includes blocks 601, 602, 603,and 604.

Block 601 may be similar to block 402 shown in FIG. 4, where theprocessor 120 may pre-process fragments of the patient authored textcorpus 106 associated with the entity 110.

The process to classify a current engagement stage may proceed fromblock 601 to block 602. At block 602, the processor 120 may call theknowledge based system 130 to inquire the current patient engagementstage of the entity 110. In some examples, when the current engagementstage of the entity 110 is not stored in the engagement database 320,the processor 120 may send the pre-processed data from the block 601 tothe knowledge based system 130. The processor 120 may analyze thepre-processed data received at the knowledge based system 130 todetermine the current engagement stage of the entity 110. The processor120 may classify the received engagement stage within an engagementprocess. For example, the processor 120 may classify the receivedengagement stage into one of the stages among an engagement processincluding the stages inform, engage, empower, collaborate, and support.

The process to classify a current engagement stage may proceed fromblock 602 to block 603. At block 603, the processor 120 may stored thecurrent engagement stage determined by the knowledge based system 130 inthe engagement database 320.

In some examples, prior to block 602, the current engagement stage ofthe entity 110 may be stored in the engagement database 320, such thatthe processor 120 may identify the current engagement stage from theengagement database 320.

The process to classify a current engagement stage may proceed fromblock 603 to block 604. At block 604, the processor 120 may stored theannotated text data 108 in accordance with the current engagement stagein the knowledge database 210. For example, particular portions of theannotated text data 108 may be associated with the determined currentengagement stage. The processor 120 may store the portions of theannotated text data 108 in the knowledge database 210 for use at a latertime, such as retrieving the stored portions of the annotated text data108 from the knowledge database 210 during a future update of theknowledge based system 130. By updating the knowledge based system 130using stored annotated text data 108 associated with various engagementstages, the knowledge base 134 may be updated to include data verifyingmappings between particular behaviors of the patients with particularengagement stages.

FIG. 7 illustrates an example process of implementing the example systemof FIG. 1 to create a knowledge base, arranged in accordance with atleast some embodiments described herein. FIG. 7 may be described belowwith references to the above descriptions of FIGS. 1-6.

The processor 120 of the system 100 may create the knowledge base 134 byimplementing an example process shown in FIG. 7, which includes blocks701, 702, 703, 704, and 705. The process shown in FIG. 7 may be aprocess to create a knowledge base associated with a particular patient,such as the entity 110.

At block 701, the processor 120 may obtain the patient profile 140 ofthe entity 110. If the patient profile 140 is stored in the knowledgedatabase 210, the processor 120 may retrieve the patient profile 140from the knowledge database 210. If the patient profile 140 is notstored in the knowledge database 210, the processor 120 may determinethe patient profile 140 as described above.

At block 702, the processor 120 may determine if the patient profile 140obtained from block 701 is received for a first time. For example, theprocessor 120 may determine if the patient profile 140 is stored in theknowledge database 210. If the patient profile 140 is received for thefirst time, the creation of the knowledge base 134 may proceed to block704. If the patient profile 140 is received for the second or subsequenttime, the creation of the knowledge base 134 may proceed to block 703.

At block 704, when the patient profile 140 is received for the firsttime, the processor 120 may develop the inference engine 132 and createa new knowledge base for the entity 110. For example, the processor 120may populate the new knowledge base with data from the newly obtainedpatient profile. The processor 120 may further develop the inferenceengine 132, such as by defining specific inference rules that may beindicated in the newly obtained patient profile. In some examples, theprocessor 120 may update the knowledge base 134 based on the newlyobtained patient profile.

At block 703, when the patient profile 140 is received for the second orsubsequent times, the processor 120 may obtain the engagement degree 160and the engagement score 170 of the entity 110, which may be stored inthe engagement database 320. In some examples, the processor 120 maydetermine an updated engagement degree 160 and an updated engagementscore 170.

Subsequent to block 703, at block 705, the processor 120 may update theinference engine 132 and the knowledge base 134 based on the patientprofile 140 obtained from block 701, and based on the engagement degree160 and the engagement score 170 obtained from block 703. For example,if the entity 110 has a low engagement degree, the processor 120 maypopulate the knowledge base 134 with facts indicating that the entity110 may be unwilling to engage in treatments under particularsituations.

FIG. 8 illustrates an example process of implementing the example systemof FIG. 1 to generate a patient engagement communicative strategy,arranged in accordance with at least some embodiments described herein.FIG. 8 may be described below with references to the above descriptionsof FIGS. 1-7.

The processor 120 of the system 100 may generate the patient engagementcommunicative strategy 180 by implementing an example process shown inFIG. 8, which includes blocks 801, 802, 803, 804, and 805. The exampleprocess shown in FIG. 8 may be triggered by the receipt of the request105 at the system 100.

At block 801, the processor 120 may obtain the patient profile 140, acurrent engagement stage of the entity 110, the engagement degree 160,and the engagement score 170, from the engagement database 320.

At block 802, the processor 120 may call the knowledge based system 130to request a next engagement stage of the entity 110. For example, ifthe engagement process being implemented on the entity 110 includes thestages inform, engage, empower, collaborate, and support, a currentengagement stage may be “inform” and a next engagement stage may be“engage”. The knowledge base 134 may include data representing theengagement process being implemented on the entity 110, and may includedata representing the engagement stages among the engagement process.The inference engine 132 may be include inference rules to detect asequence of the engagement stages. As such, the processor 120 may sendthe current engagement stage to the knowledge based system 130, and theinference engine 132 may include inference rules for processor 120 toidentify a next engagement stage from the knowledge base 134.

At block 803, the processor 120 may request the knowledge based system130 to determine a communicative structure for the next engagement stageidentified from block 802. For example, if the next engagement stage is“engage”, the processor 120 may infer at least a communication tool, acommunication style, and/or other communication characteristics,preferred by the entity 110 during the engagement stage of “engage”. Theprocessor 120 may store the inferred communication structure in theengagement database 320. In some examples, the inferred communicationstructure may be reflective of a strategy to proceed to the patientengagement of the entity 110 from the current engagement stage to thenext engagement stage.

In some examples, the processor 120 may update the engagement score 170in order for the engagement score 170 to reflect a predictedeffectiveness of the strategy to proceed from the current engagementstage to the next engagement stage. For example, if the entity 110 isnot fluent in a first language but is fluent is a second language, afirst score of a first strategy to use the first language to communicatewith the entity 110 may be lower than a second score of a secondstrategy to use the second language to communicate with the entity 110.

At block 804, the processor 120 may store the engagement degree 160 andthe engagement score 170 in the engagement database 320.

At block 805, the processor may generate the patient engagementcommunicative strategy 180 by aggregating one or more of the patientprofile, the engagement degree 160, the engagement score 170, and thecommunication structure stored in the engagement database 320. Thepatient engagement communicative strategy 180 may specify one or more ofthe patient profile 140, the current engagement stage 160, the currentengagement stage, the next engagement stage, the engagement degree 170,and a communication scheme to communicate with the entity 110, where thecommunication scheme includes a suggestion of strategies for achievingthe next engagement stage.

FIG. 9 illustrates a flow diagram relating to patient engagementcommunicative strategy recommendation, arranged in accordance with atleast some embodiments presented herein. The process in FIG. 9 may beimplemented using, for example, computer system 100 discussed above. Anexample process may include one or more operations, actions, orfunctions as illustrated by one or more of blocks 902, 904, 906, 908,910, 912, 914, 916, 918, and/or 920. Although illustrated as discreteblocks, various blocks may be divided into additional blocks, combinedinto fewer blocks, eliminated, or performed in parallel, depending onthe desired implementation.

Processing may begin at block 902, where a processor may obtain apatient authored text corpus from a plurality of data sources.

Processing may continue from block 902 to block 904. At block 904, theprocessor may obtain patient authored health data from the plurality ofdata sources.

Processing may continue from blocks 904 to block 906. At block 906, theprocessor may train a communicative model based on the patient authoredtext corpus. In some examples, the processor may annotate the patientauthored text corpus based on a health behavior model and acommunicative taxonomy to generate annotated text data, and may trainthe communicative model using the annotated text data.

Processing may continue from blocks 906 to block 908. At block 908, theprocessor may generate at least one patient profile based on the patientauthored text corpus and the patient authored health data.

Processing may continue from blocks 908 to block 910. At block 910, theprocessor may construct a knowledge based system based on thecommunicative model and the at least one patient profile. The knowledgebased system may include an inference engine and a knowledge base.

Processing may continue from blocks 910 to block 912. At block 912, theprocessor may receive a request for an engagement communicative strategyassociated with an entity.

Processing may continue from blocks 912 to block 914. At block 914, theprocessor may retrieve a patient profile of the entity from the at leastone patient profile.

Processing may continue from blocks 914 to block 916. At block 916, theprocessor may input the patient profile, an engagement degree, and anengagement score to the knowledge based system. The engagement degreemay be representative of a level of engagement of the entity in apatient engagement process, and the engagement score may berepresentative of an effectiveness of a strategy to improve the level ofengagement of the entity in the patient engagement process

Processing may continue from blocks 916 to block 918. At block 918, theprocessor may execute the knowledge based system to determine theengagement communicative strategy associated with the entity based onthe patient profile, the engagement degree, and the engagement score.

Processing may continue from blocks 918 to block 920. At block 920, theprocessor may output the engagement communicative strategy. Theengagement communicative strategy specify one or more of a communicationscheme to communicate with the entity, the patient profile, the currentengagement stage, a next engagement stage, the engagement degree, andthe engagement score. The communication scheme may include a suggestionof strategies for achieving the next engagement stage.

In some examples, the processor may perform the blocks 902, 904, 906,908, and 910 continuously in order to retrain the communicative model,and to update the knowledge base of the knowledge based system when newdata is received at blocks 902 and 904. The processor may perform theblocks 902, 904, 906, 908, and 910 continuously, regardless of theoperation status of blocks 912, 914, 916, 918, and 920. For example,operations of the blocks 912, 914, 916, 918, and 920 may continue evenif the processor is retraining the communicative model, and receipt ofthe request at block 912 may not stop the retraining process.

FIG. 10 illustrates a schematic of an example computer or processingsystem that may implement patient engagement communicative strategyrecommendation in one embodiment of the present disclosure. The computersystem is only one example of a suitable processing system and is notintended to suggest any limitation as to the scope of use orfunctionality of embodiments of the methodology described herein. Theprocessing system shown may be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with the processingsystem shown in FIG. 6 may include, but are not limited to, personalcomputer systems, server computer systems, thin clients, thick clients,handheld or laptop devices, multiprocessor systems, microprocessor-basedsystems, set top boxes, programmable consumer electronics, network PCs,minicomputer systems, mainframe computer systems, supercomputers, anddistributed cloud computing environments that include any of the abovesystems or devices, and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may include a module 30 (e.g.,engagement communicative strategy module 30) that performs the methodsdescribed herein. The module 30 may be programmed into the integratedcircuits of the processor 12, or loaded from memory 16, storage device18, or network 24 or combinations thereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

FIG. 11 depicts a cloud computing environment according to an embodimentof the present invention. It is to be understood that although thisdisclosure includes a detailed description on cloud computing,implementation of the teachings recited herein are not limited to acloud computing environment. Rather, embodiments of the presentinvention are capable of being implemented in conjunction with any othertype of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 11, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 11 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

FIG. 12 depicts abstraction model layers according to an embodiment ofthe present invention. Referring now to FIG. 12, a set of functionalabstraction layers provided by cloud computing environment 50 (FIG. 11)is shown. It should be understood in advance that the components,layers, and functions shown in FIG. 8 are intended to be illustrativeonly and embodiments of the invention are not limited thereto. Asdepicted, the following layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and engagement communicative strategydetermination 96.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method for outputting an engagement communicative strategy, the method comprising: receiving, by a processor, a patient authored text corpus from a data source external to the processor, wherein the patient authored text corpus is structured text data; receiving, by the processor, patient authored health data; training, by the processor, a communicative model based on the patient authored text corpus; generating, by the processor, a set of patient profiles based on the patient authored text corpus and the patient authored health data; constructing, by the processor, a knowledge based system based on the communicative model and the set of patient profiles, wherein the knowledge based system includes an inference engine and a knowledge base, the inference engine including a set of inference rules executable by the processor to infer facts in the knowledge base, wherein constructing the knowledge based system includes populating the knowledge base with outcomes determined by the communicative model, the knowledge base is represented as an ontology including a set of objects representing the facts in the knowledge base, and each object includes one or more pointers that point to another object among the knowledge base; deploying, by the processor, the knowledge based system to a device implementing an interactive application; receiving, by the processor from the device implementing the interactive application, a request for an engagement communicative strategy associated with an entity, the request being inputted by a user of the device; retrieving, by the processor, a patient profile of the entity from the set of patient profiles; inputting, by the processor, the patient profile, an engagement degree, and an engagement score to the knowledge based system, wherein the engagement degree is representative of a level of engagement of the entity in a patient engagement process, and the engagement score is representative of an effectiveness of a strategy to improve the level of engagement of the entity in the patient engagement process; executing, by the processor, the inference engine of the knowledge based system to infer facts in the knowledge base relating to the engagement communicative strategy associated with the entity based on the patient profile, the engagement degree, and the engagement score; and outputting, by the processor, the engagement communicative strategy to the device implementing the interactive application, wherein the engagement communicative strategy specifies a communication scheme for the user to communicate with the entity.
 2. The method of claim 1, further comprising: executing, by the processor, a set of executable code stored in a memory to perform: selecting, based on selection criteria indicated by the set of instructions, a particular health behavior change model from a plurality of health behavior change models stored in the memory; identifying, according to the set of instructions stored in the memory, a communication taxonomy among a plurality of communication taxonomies; annotating, by the processor, the patient authored text corpus based on the particular health behavior model and the identified communicative taxonomy to generate annotated text data; and performing a supervised machine learning technique to train the communicative model using the annotated text data as training data.
 3. The method of claim 1, further comprising: determining, by the processor, whether the patient authored text corpus is received for the first time; in response to the patient authored text corpus being received for the first time: annotating, by the processor, the patient authored text corpus based on a health behavior model and a communicative taxonomy to generate annotated text data; storing, by the processor, the annotated text data in a knowledge database; in response to the patient authored text corpus not being received for the first time: retrieving, by the processor, the annotated text data from the knowledge database; wherein training the communicative model includes training the communicative model using the annotated text data.
 4. The method of claim 1, further comprising: receiving, by the processor, new patient authored text corpus; receiving, by the processor, new patient authored health data; retraining, by the processor, the communicative model based on the new patient authored text corpus; updating, by the processor, the set of patient profiles based on the new patient authored text corpus and the new patient authored health data; and updating, by the processor, the knowledge based system using the retrained communicative model and the updated patient profiles.
 5. The method of claim 1, wherein the engagement communicative strategy further specifies at least one of: the patient profile; the current engagement stage; a next engagement stage; the engagement degree; and the engagement score, wherein the communication scheme includes a suggestion of strategies for achieving the next engagement stage.
 6. The method of claim 1, further comprising: receiving, by the processor, a request for an engagement communicative strategy associated with a population; retrieving, by the processor, a subset of patient profiles among the set of patient profiles, wherein the subset of patient profiles is associated with entities among the population; inputting, by the processor, the subset of patient profiles, a set of engagement degree associated with the population, and a set of engagement scores associated with the population, to the knowledge based system; executing, by the processor, the knowledge based system to determine an engagement communicative strategy associated with the population; and outputting, by the processor, the engagement communicative strategy associated with the population, wherein the engagement communicative strategy specifies a communication scheme to communicate with the entities among the population.
 7. The method of claim 6, wherein the communication scheme to communicate with the entities among the population includes a suggestion to prioritize communication with particular entities among the population.
 8. A system comprising: a memory configured to store a set of engagement instructions; a processor configured to be in communication with the memory, the processor being configured to execute the set of engagement instructions stored in the memory to: receive a patient authored text corpus from a data source external to the processor, wherein the patient authored text corpus is structured text data; receive patient authored health data; train a communicative model based on the patient authored text corpus; generate a set of patient profiles based on the patient authored text corpus and the patient authored health data; construct a knowledge based system based on the communicative model and the set of patient profiles, wherein the construction of the knowledge based system includes populating the knowledge base with outcomes determined by the communicative model, the knowledge based system includes an inference engine and a knowledge base, the inference engine including a set of inference rules executable by the processor to infer facts in the knowledge base, wherein the knowledge base is represented as an ontology including a set of objects representing the facts in the knowledge base, and each object includes one or more pointers that point to another object among the knowledge base; deploy the knowledge based system to a device implementing an interactive application; receive, from the device implementing the interactive application, a request for an engagement communicative strategy associated with an entity, the request being inputted by a user of the device; retrieve a patient profile of the entity from the set of patient profiles; input the patient profile, an engagement degree, and an engagement score to the knowledge based system, wherein the engagement degree is representative of a level of engagement of the entity in a patient engagement process, and the engagement score is representative of an effectiveness of a strategy to improve the level of engagement of the entity in the patient engagement process; execute the inference engine of the knowledge based system to infer facts in the knowledge base relating to the engagement communicative strategy associated with the entity based on the patient profile, the engagement degree, and the engagement score; and output the engagement communicative strategy to the device implementing the interactive application, wherein the engagement communicative strategy specifies a communication scheme for the user to communicate with the entity.
 9. The system of claim 8, wherein the processor is further configured to: execute a set of executable code stored in a memory to: select, based on selection criteria indicated by the set of instructions, a particular health behavior change model from a plurality of health behavior change models stored in the memory; identify, according to the set of instructions stored in the memory, a communication taxonomy among a plurality of communication taxonomies; annotate the patient authored text corpus based on the particular health behavior model and the identified communicative taxonomy to generate annotated text data; and perform a supervised machine learning technique to train the communicative model using the annotated text data as training data.
 10. The system of claim 8, wherein the processor is further configured to: determine whether the patient authored text corpus is received for the first time; in response to the patient authored text corpus being received for the first time: annotate the patient authored text corpus based on a health behavior model and a communicative taxonomy to generate annotated text data; store in a knowledge database; in response to the patient authored text corpus not being received for the first time: retrieve the annotated text data from the knowledge database; wherein training the communicative model includes training the communicative model using the annotated text data.
 11. The system of claim 8, wherein the processor is further configured to: receive new patient authored text corpus; receive new patient authored health data; retrain the communicative model based on the new patient authored text corpus; update the set of patient profiles based on the new patient authored text corpus and the new patient authored health data; and update the knowledge based system using the retrained communicative model and the updated patient profiles.
 12. The system of claim 8, wherein the engagement communicative strategy further specifies at least one of: the patient profile; the current engagement stage; a next engagement stage; the engagement degree; and the engagement score, wherein the communication scheme includes a suggestion of strategies for achieving the next engagement stage.
 13. The system of claim 8, wherein the processor is further configured to: receive a request for an engagement communicative strategy associated with a population; retrieve a subset of patient profiles among the set of patient profiles, wherein the subset of patient profiles is associated with entities among the population; input the subset of patient profiles, a set of engagement degree associated with the population, and a set of engagement scores associated with the population, to the knowledge based system; execute the knowledge based system to determine an engagement communicative strategy associated with the population; and output the engagement communicative strategy associated with the population, wherein the engagement communicative strategy specifies a communication scheme to communicate with the entities among the population.
 14. The system of claim 13, wherein the communication scheme to communicate with the entities among the population includes a suggestion to prioritize communication with particular entities among the population.
 15. A computer program product for outputting an engagement communicative strategy, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing element of a device to cause the device to: receive a patient authored text corpus from a data source external to the processor, wherein the patient authored text corpus is structured text data; receive patient authored health data; train a communicative model based on the patient authored text corpus; generate a set of patient profiles based on the patient authored text corpus and the patient authored health data; construct a knowledge based system based on the communicative model and the set of patient profiles, wherein the construction of the knowledge based system includes populating the knowledge base with outcomes determined by the communicative model the knowledge based system includes an inference engine and a knowledge base, the inference engine including a set of inference rules executable by the processor to infer facts included in the knowledge base, wherein the knowledge base is represented as an ontology including a set of objects representing the facts in the knowledge base, and each object includes one or more pointers that point to another object among the knowledge base; deploy the knowledge based system to a device implementing an interactive application; receive, from the device implementing the interactive application, a request for an engagement communicative strategy associated with an entity, the request being inputted by a user of the device; retrieve a patient profile of the entity from the set of patient profiles; input the patient profile, an engagement degree, and an engagement score to the knowledge based system, wherein the engagement degree is representative of a level of engagement of the entity in a patient engagement process, and the engagement score is representative of an effectiveness of a strategy to improve the level of engagement of the entity in the patient engagement process; execute the inference engine of the knowledge based system to infer facts in the knowledge base relating to the engagement communicative strategy associated with the entity based on the patient profile, the engagement degree, and the engagement score; and output the engagement communicative strategy to the device implementing the interactive application, wherein the engagement communicative strategy specifies a communication scheme for the user to communicate with the entity.
 16. The computer program product of claim 15, wherein the program instructions are further executable by the processing element of the device to cause the device to: execute a set of executable code stored in a memory to: select, based on selection criteria indicated by the set of instructions, a particular health behavior change model from a plurality of health behavior change models stored in the memory; identify, according to the set of instructions stored in the memory, a communication taxonomy among a plurality of communication taxonomies; annotate the patient authored text corpus based on the particular health behavior model and the identified communicative taxonomy to generate annotated text data; and perform a supervised machine learning technique to train the communicative model using the annotated text data as training data.
 17. The computer program product of claim 15, wherein the program instructions are further executable by the processing element of the device to cause the device to: determine whether the patient authored text corpus is received for the first time; in response to the patient authored text corpus being received for the first time: annotate the patient authored text corpus based on a health behavior model and a communicative taxonomy to generate annotated text data; store the annotated text data in a knowledge database; in response to the patient authored text corpus not being received for the first time: retrieve the annotated text data from the knowledge database; wherein training the communicative model includes training the communicative model using the annotated text data.
 18. The computer program product of claim 15, wherein the program instructions are further executable by the processing element of the device to cause the device to: receive new patient authored text corpus; receive new patient authored health data; retrain the communicative model based on the new patient authored text corpus; update the set of patient profiles based on the new patient authored text corpus and the new patient authored health data; and update the knowledge base using the retrained communicative model and the updated patient profiles.
 19. The computer program product of claim 15, wherein the engagement communicative strategy further specifies at least one of: the patient profile; the current engagement stage; a next engagement stage; the engagement degree; and the engagement score, wherein the communication scheme includes a suggestion of strategies for achieving the next engagement stage.
 20. The computer program product of claim 15, wherein the program instructions are further executable by the processing element of the device to cause the device to: receive a request for an engagement communicative strategy associated with a population; retrieve a subset of patient profiles among the set of patient profiles, wherein the subset of patient profiles is associated with entities among the population; input the subset of patient profiles, a set of engagement degree associated with the population, and a set of engagement scores associated with the population, to the knowledge based system; execute the knowledge based system to determine an engagement communicative strategy associated with the population; and output the engagement communicative strategy associated with the population, wherein the engagement communicative strategy specifies a communication scheme to communicate with the entities among the population. 